<h2 id="vae-metrics-lab-(gui)">VAE Metrics Lab (GUI)</h2><a target="_blank" rel="ugc" href="https://civarchive.com/articles/30734/scentific-breakdown-of-the-vaes-onsite-million-buzz-earners">SDXL Breakdown</a><h1 id="vae-metrics-lab">VAE Metrics Lab</h1>This tool is a VAE evaluation and reconstruction fidelity benchmarking system that compares original input images against VAE reconstructions using both perceptual and signal-level metrics.It takes a folder of images, preprocesses them into a consistent format, and optionally embeds a small color calibration marker into the input images. A VAE then reconstructs these images, and the system compares the outputs back to the original ground truth.<hr /><h2 id="evaluation-metrics">Evaluation Metrics</h2>The system evaluates reconstruction quality using:<ul><li>LPIPS perceptual distance – measures human-perceived visual similarity between images</li><li>Gradient energy – evaluates edge and fine-detail preservation</li><li>FFT-based structure analysis – measures frequency-domain similarity and global structure fidelity</li><li>Color diversity metrics – estimates texture richness and reconstruction entropy</li><li>RGB pixel error analysis – measures direct per-channel reconstruction accuracy</li><li>Black/white/RGB marker analysis – detects brightness bias, contrast scaling, and per-channel color drift using a fixed calibration patch</li></ul><hr /><h2 id="outputs">Outputs</h2>The tool produces:<ul><li>A per-image CSV report</li><li>A summary JSON file with averaged metrics</li></ul>These outputs allow direct comparison between different VAEs in terms of:<ul><li>reconstruction quality</li><li>color fidelity</li><li>structural accuracy</li><li>calibration stability</li></ul><hr /><h1 id="math-for-the-evaluation">Math for the Evaluation</h1>Let x in R^(H x W x 3). Define reconstruction:x_hat = R(x) = D(E(x))<hr /><h2 id="metrics">Metrics</h2>Perceptual loss: L_perc = d_phi(x, x_hat)Gradient energy: G(x) = E[ |grad g(x)|^2 ] rho_G = G(x_hat) / G(x)Color support: C(x) = number of unique RGB values in x rho_C = C(x_hat) / C(x)Brightness bias: b = E[ m_black(x_hat) - m_black(x) ]Contrast gain: gamma = E[ m_white(x_hat) - m_black(x_hat) ] / E[ m_white(x) - m_black(x) ]Channel drift: delta_c = E[ c(x_hat) - c(x) ], c in {R,G,B}<hr /><h2 id="final-score">Final score</h2>J = lambda1 * L_perc<ul><li>lambda2 * abs(1 - rho_G)</li><li>lambda3 * abs(1 - rho_C)</li><li>lambda4 * norm(delta_c)</li><li>lambda5 * abs(b)</li><li>lambda6 * abs(gamma - 1)</li></ul><hr /><h2 id="system-model">System model</h2>R(x) approx x

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